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Minimizing fuel consumption is emerging as the next major challenge for engine control and calibration, even as the requirements of complying with ever lower transient emissions regulations cannot be underestimated. Meeting these difficult and apparently conflicting emissions and efficiency goals is becoming increasingly onerous as engine and aftertreatment control complexity increases. Conventional engine calibration techniques are by nature time-intensive, ad-hoc and repetitive, resulting in low productivity of test facilities and engineering effort. Steady state engine mapping methods, such as design of experiments, do little to ensure transient emissions compliance or fuel consumption optimization. A new model-based Rapid Transient Calibration system has been developed, tested and validated using a 2007 production-specification Detroit Diesel Series 60 heavy-duty diesel engine.

Fuel efficiency optimization has emerged as the next major challenge in the field of diesel engine control and calibration. Conventional techniques will not be able to accommodate the many rigorous and competing demands placed on next generation engine control. An approach that holds great promise for future advanced transient engine control is model-based control (MBC). Model-based calibration optimization methods have shown their efficacy in the off-line engine development process, and these techniques can be extended to online, real-time engine control. This paper describes preliminary results from the deployment of MBC on a next generation heavy duty diesel engine. A description of the development process, the technologies that have been developed to exploit this process and the resultant proof-of-concept emissions and fuel consumption validation are provided.

A project has been undertaken to optimize the engine control software calibration of a modern heavy-duty diesel engine for operation with gas-to-liquids (GTL) diesel fuel, with the objective of developing an understanding of the scope for optimization with this fuel, which has different physical and combustion properties to that of conventional, crude-derived diesel. A data-driven, model-based calibration technique utilizing artificial neural networks was used to develop optimized transient and steady-state calibrations with both conventional diesel fuel, as well as neat GTL fuel. The engine control parameters that were optimized were injection timing, exhaust gas recirculation rate, rail pressure, and charge mass. The optimization aimed to minimize fuel consumption without deterioration in engine-out nitrogen oxide (NOx) and soot emissions. This paper reports on the calibration optimization methodology employed and the results achieved to date.

With the adoption of complex technologies such as multiple injections, EGR and variable geometry turbocharging, it has become increasingly onerous to develop optimal engine control calibrations for either light- or heavy-duty diesel engines. The addition of NOx and PM aftertreatment systems increases further the calibration burden, as both diesel particulate filters and NOx absorbers require regeneration initiated by the engine management system. There is significant interest in the industry in reducing development costs by moving as much of the engine calibration process as is feasible from the engine test cell to the virtual desktop environment. This paper describes the development of a model-based calibration optimization system that offers significant advantages in reducing the time and effort required to obtain certification-quality engine calibrations.

Pending GHG emissions reduction legislation for medium- and heavy-duty vehicles will require the development of engines and powertrains with significantly increased mechanical and electronic complexity. Increasing powertrain efficiency will require the simulation, control and calibration of an expanding number of highly interdependent air, fuel, exhaust, combustion and energy transfer subsystems. As a result of these increases in complexity, engine and powertrain control is becoming significantly more sophisticated and costly to develop and difficult to optimize. The high cost of developing engines and powertrain systems that demonstrate greater fuel efficiency and emissions benefits than the engines of today, is undeniable. The increased calibration burden and the complexity of optimization require the development and adoption of entirely new methods for transient engine calibration and optimization to achieve maximum vehicle fuel efficiency and lowest regulated emissions.